Figure 8 - statistical illustration showing breakdown by land cover
- Different ag types vs urban
- Heterogeneity within urban
#, echo=FALSE, warning=FALSE, include = FALSE
all_pixels <- readRDS(here::here("risk_maps",
"data",
"processed_data",
"all_pixels.RData"))
ag_types <- c("Vegetable", "Fruit Tree", "Fruit", "Fodder", "Uncultivated", "Nuts")
filter(all_pixels, landcover %in% "Urban") %>%
ggplot(aes(x = transmission_prob, fill= time_of_day, alpha = 0.2)) +
geom_density()

filter(all_pixels, landcover %in% "Urban") %>%
ggplot(aes(x = air_temperature, fill= time_of_day, alpha = 0.2)) +
geom_density()

filter(all_pixels, landcover %in% ag_types) %>%
ggplot(aes(x = transmission_prob, fill= time_of_day, alpha = 0.2)) +
geom_density()

filter(all_pixels, landcover %in% ag_types) %>%
ggplot(aes(x = transmission_prob, fill= time_of_day, alpha = 0.2)) +
geom_density()

filter(all_pixels, time_of_day == "Day", landcover %in% ag_types) %>%
ggplot(aes(x = transmission_prob, fill= landcover, alpha = 0.2)) +
geom_density()

filter(all_pixels, time_of_day == "Day", landcover %in% ag_types) %>%
ggplot(aes(x = biting_rate, fill= landcover, alpha = 0.2)) +
geom_density()
## Warning: Removed 383617 rows containing non-finite values (stat_density).

filter(all_pixels, time_of_day == "Day", landcover %in% ag_types) %>%
ggplot(aes(x = air_temperature, fill= landcover, alpha = 0.2)) +
geom_density()
